In recent years, data visualization has become an increasingly important part of data analysis. Visualization, for example, enables companies and other organizations to meaningfully present raw data to facilitate effective and efficient analysis of the data. Obtaining and/or processing the raw data to produce a visualization of the data, however, can be a challenge. When visualizing “big data,” for example, the costs related to obtaining and/or processing all of the data may be substantial. While advances in the performance of computer hardware have greatly increased the capabilities of servers and networks to obtain and process data as well as lowered the costs to do so, the amount of data available to be obtained and processed has grown exponentially in comparison to any advances in hardware performance. Moreover, because obtaining and/or processing of such data may be time consuming, users of typical data visualization systems generally experience considerable delay before being provided with substantive information related to their requests. These and other drawbacks exist.